Translate all code to English (#1836)

* Review the EN heading format.

* Fix pythontutor headings.

* Fix pythontutor headings.

* bug fixes

* Fix headings in **/summary.md

* Revisit the CN-to-EN translation for Python code using Claude-4.5

* Revisit the CN-to-EN translation for Java code using Claude-4.5

* Revisit the CN-to-EN translation for Cpp code using Claude-4.5.

* Fix the dictionary.

* Fix cpp code translation for the multipart strings.

* Translate Go code to English.

* Update workflows to test EN code.

* Add EN translation for C.

* Add EN translation for CSharp.

* Add EN translation for Swift.

* Trigger the CI check.

* Revert.

* Update en/hash_map.md

* Add the EN version of Dart code.

* Add the EN version of Kotlin code.

* Add missing code files.

* Add the EN version of JavaScript code.

* Add the EN version of TypeScript code.

* Fix the workflows.

* Add the EN version of Ruby code.

* Add the EN version of Rust code.

* Update the CI check for the English version  code.

* Update Python CI check.

* Fix cmakelists for en/C code.

* Fix Ruby comments

en/codes/python/chapter_searching/binary_search.py

@@ -6,36 +6,36 @@ Author: timi (xisunyy@163.com)
 
 
 def binary_search(nums: list[int], target: int) -> int:
-    """Binary search (double closed interval)"""
-    # Initialize double closed interval [0, n-1], i.e., i, j point to the first element and last element of the array respectively
+    """Binary search (closed interval)"""
+    # Initialize closed interval [0, n-1], i.e., i, j point to the first and last elements of the array
     i, j = 0, len(nums) - 1
-    # Loop until the search interval is empty (when i > j, it is empty)
+    # Loop, exit when the search interval is empty (empty when i > j)
     while i <= j:
-        # Theoretically, Python's numbers can be infinitely large (depending on memory size), so there is no need to consider large number overflow
-        m = i + (j - i) // 2  # Calculate midpoint index m
+        # In theory, Python numbers can be infinitely large (depending on memory size), no need to consider large number overflow
+        m = (i + j) // 2  # Calculate midpoint index m
         if nums[m] < target:
-            i = m + 1  # This situation indicates that target is in the interval [m+1, j]
+            i = m + 1  # This means target is in the interval [m+1, j]
         elif nums[m] > target:
-            j = m - 1  # This situation indicates that target is in the interval [i, m-1]
+            j = m - 1  # This means target is in the interval [i, m-1]
         else:
-            return m  # Found the target element, thus return its index
-    return -1  # Did not find the target element, thus return -1
+            return m  # Found the target element, return its index
+    return -1  # Target element not found, return -1
 
 
 def binary_search_lcro(nums: list[int], target: int) -> int:
-    """Binary search (left closed right open interval)"""
-    # Initialize left closed right open interval [0, n), i.e., i, j point to the first element and the last element +1 of the array respectively
+    """Binary search (left-closed right-open interval)"""
+    # Initialize left-closed right-open interval [0, n), i.e., i, j point to the first element and last element+1
     i, j = 0, len(nums)
-    # Loop until the search interval is empty (when i = j, it is empty)
+    # Loop, exit when the search interval is empty (empty when i = j)
     while i < j:
-        m = i + (j - i) // 2  # Calculate midpoint index m
+        m = (i + j) // 2  # Calculate midpoint index m
         if nums[m] < target:
-            i = m + 1  # This situation indicates that target is in the interval [m+1, j)
+            i = m + 1  # This means target is in the interval [m+1, j)
         elif nums[m] > target:
-            j = m  # This situation indicates that target is in the interval [i, m)
+            j = m  # This means target is in the interval [i, m)
         else:
-            return m  # Found the target element, thus return its index
-    return -1  # Did not find the target element, thus return -1
+            return m  # Found the target element, return its index
+    return -1  # Target element not found, return -1
 
 
 """Driver Code"""
@@ -43,10 +43,10 @@ if __name__ == "__main__":
     target = 6
     nums = [1, 3, 6, 8, 12, 15, 23, 26, 31, 35]
 
-    # Binary search (double closed interval)
+    # Binary search (closed interval)
     index = binary_search(nums, target)
     print("Index of target element 6 =", index)
 
-    # Binary search (left closed right open interval)
+    # Binary search (left-closed right-open interval)
     index = binary_search_lcro(nums, target)
     print("Index of target element 6 =", index)

en/codes/python/chapter_searching/binary_search_edge.py

@@ -15,7 +15,7 @@ def binary_search_left_edge(nums: list[int], target: int) -> int:
     """Binary search for the leftmost target"""
     # Equivalent to finding the insertion point of target
     i = binary_search_insertion(nums, target)
-    # Did not find target, thus return -1
+    # Target not found, return -1
     if i == len(nums) or nums[i] != target:
         return -1
     # Found target, return index i
@@ -28,7 +28,7 @@ def binary_search_right_edge(nums: list[int], target: int) -> int:
     i = binary_search_insertion(nums, target + 1)
     # j points to the rightmost target, i points to the first element greater than target
     j = i - 1
-    # Did not find target, thus return -1
+    # Target not found, return -1
     if j == -1 or nums[j] != target:
         return -1
     # Found target, return index j
@@ -41,9 +41,9 @@ if __name__ == "__main__":
     nums = [1, 3, 6, 6, 6, 6, 6, 10, 12, 15]
     print(f"\nArray nums = {nums}")
 
-    # Binary search for left and right boundaries
+    # Binary search for left boundary and right boundary
     for target in [6, 7]:
         index = binary_search_left_edge(nums, target)
-        print(f"The index of the leftmost element {target} is {index}")
+        print(f"Index of the leftmost element {target} is {index}")
         index = binary_search_right_edge(nums, target)
-        print(f"The index of the rightmost element {target} is {index}")
+        print(f"Index of the rightmost element {target} is {index}")

en/codes/python/chapter_searching/binary_search_insertion.py

@@ -7,30 +7,30 @@ Author: krahets (krahets@163.com)
 
 def binary_search_insertion_simple(nums: list[int], target: int) -> int:
     """Binary search for insertion point (no duplicate elements)"""
-    i, j = 0, len(nums) - 1  # Initialize double closed interval [0, n-1]
+    i, j = 0, len(nums) - 1  # Initialize closed interval [0, n-1]
     while i <= j:
-        m = i + (j - i) // 2  # Calculate midpoint index m
+        m = (i + j) // 2  # Calculate midpoint index m
         if nums[m] < target:
-            i = m + 1  # Target is in interval [m+1, j]
+            i = m + 1  # target is in the interval [m+1, j]
         elif nums[m] > target:
-            j = m - 1  # Target is in interval [i, m-1]
+            j = m - 1  # target is in the interval [i, m-1]
         else:
             return m  # Found target, return insertion point m
-    # Did not find target, return insertion point i
+    # Target not found, return insertion point i
     return i
 
 
 def binary_search_insertion(nums: list[int], target: int) -> int:
     """Binary search for insertion point (with duplicate elements)"""
-    i, j = 0, len(nums) - 1  # Initialize double closed interval [0, n-1]
+    i, j = 0, len(nums) - 1  # Initialize closed interval [0, n-1]
     while i <= j:
-        m = i + (j - i) // 2  # Calculate midpoint index m
+        m = (i + j) // 2  # Calculate midpoint index m
         if nums[m] < target:
-            i = m + 1  # Target is in interval [m+1, j]
+            i = m + 1  # target is in the interval [m+1, j]
         elif nums[m] > target:
-            j = m - 1  # Target is in interval [i, m-1]
+            j = m - 1  # target is in the interval [i, m-1]
         else:
-            j = m - 1  # First element less than target is in interval [i, m-1]
+            j = m - 1  # The first element less than target is in the interval [i, m-1]
     # Return insertion point i
     return i
 
@@ -43,7 +43,7 @@ if __name__ == "__main__":
     # Binary search for insertion point
     for target in [6, 9]:
         index = binary_search_insertion_simple(nums, target)
-        print(f"Element {target}'s insertion point index is {index}")
+        print(f"Index of insertion point for element {target} is {index}")
 
     # Array with duplicate elements
     nums = [1, 3, 6, 6, 6, 6, 6, 10, 12, 15]
@@ -51,4 +51,4 @@ if __name__ == "__main__":
     # Binary search for insertion point
     for target in [2, 6, 20]:
         index = binary_search_insertion(nums, target)
-        print(f"Element {target}'s insertion point index is {index}")
+        print(f"Index of insertion point for element {target} is {index}")

en/codes/python/chapter_searching/hashing_search.py

@@ -14,7 +14,7 @@ from modules import ListNode, list_to_linked_list
 def hashing_search_array(hmap: dict[int, int], target: int) -> int:
     """Hash search (array)"""
     # Hash table's key: target element, value: index
-    # If the hash table does not contain this key, return -1
+    # If this key does not exist in the hash table, return -1
     return hmap.get(target, -1)
 
 
@@ -23,7 +23,7 @@ def hashing_search_linkedlist(
 ) -> ListNode | None:
     """Hash search (linked list)"""
     # Hash table's key: target element, value: node object
-    # If the hash table does not contain this key, return None
+    # If this key does not exist in the hash table, return None
     return hmap.get(target, None)
 
 
@@ -48,4 +48,4 @@ if __name__ == "__main__":
         map1[head.val] = head  # key: node value, value: node
         head = head.next
     node: ListNode = hashing_search_linkedlist(map1, target)
-    print("Target node value 3's corresponding node object is", node)
+    print("The corresponding node object for target node value 3 is", node)

en/codes/python/chapter_searching/linear_search.py

@@ -13,21 +13,21 @@ from modules import ListNode, list_to_linked_list
 
 def linear_search_array(nums: list[int], target: int) -> int:
     """Linear search (array)"""
-    # Traverse array
+    # Traverse the array
     for i in range(len(nums)):
-        if nums[i] == target:  # Found the target element, thus return its index
+        if nums[i] == target:  # Found the target element, return its index
             return i
-    return -1  # Did not find the target element, thus return -1
+    return -1  # Target element not found, return -1
 
 
 def linear_search_linkedlist(head: ListNode, target: int) -> ListNode | None:
     """Linear search (linked list)"""
-    # Traverse the list
+    # Traverse the linked list
     while head:
         if head.val == target:  # Found the target node, return it
             return head
         head = head.next
-    return None  # Did not find the target node, thus return None
+    return None  # Target node not found, return None
 
 
 """Driver Code"""
@@ -42,4 +42,4 @@ if __name__ == "__main__":
     # Perform linear search in linked list
     head: ListNode = list_to_linked_list(nums)
     node: ListNode | None = linear_search_linkedlist(head, target)
-    print("Target node value 3's corresponding node object is", node)
+    print("The corresponding node object for target node value 3 is", node)

en/codes/python/chapter_searching/two_sum.py

@@ -6,8 +6,8 @@ Author: krahets (krahets@163.com)
 
 
 def two_sum_brute_force(nums: list[int], target: int) -> list[int]:
-    """Method one: Brute force enumeration"""
-    # Two-layer loop, time complexity is O(n^2)
+    """Method 1: Brute force enumeration"""
+    # Two nested loops, time complexity is O(n^2)
     for i in range(len(nums) - 1):
         for j in range(i + 1, len(nums)):
             if nums[i] + nums[j] == target:
@@ -16,10 +16,10 @@ def two_sum_brute_force(nums: list[int], target: int) -> list[int]:
 
 
 def two_sum_hash_table(nums: list[int], target: int) -> list[int]:
-    """Method two: Auxiliary hash table"""
+    """Method 2: Auxiliary hash table"""
     # Auxiliary hash table, space complexity is O(n)
     dic = {}
-    # Single-layer loop, time complexity is O(n)
+    # Single loop, time complexity is O(n)
     for i in range(len(nums)):
         if target - nums[i] in dic:
             return [dic[target - nums[i]], i]
@@ -34,9 +34,9 @@ if __name__ == "__main__":
     target = 13
 
     # ====== Driver Code ======
-    # Method one
+    # Method 1
     res: list[int] = two_sum_brute_force(nums, target)
-    print("Method one res =", res)
-    # Method two
+    print("Method 1 res =", res)
+    # Method 2
     res: list[int] = two_sum_hash_table(nums, target)
-    print("Method two res =", res)
+    print("Method 2 res =", res)